561 lines
24 KiB
Python
561 lines
24 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Plotting utils
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"""
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import contextlib
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import math
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import os
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from copy import copy
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from pathlib import Path
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from urllib.error import URLError
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import cv2
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sn
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import torch
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from PIL import Image, ImageDraw, ImageFont
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from utils import TryExcept, threaded
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from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
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is_ascii, xywh2xyxy, xyxy2xywh)
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from utils.metrics import fitness
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from utils.segment.general import scale_image
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# Settings
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RANK = int(os.getenv('RANK', -1))
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matplotlib.rc('font', **{'size': 11})
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matplotlib.use('Agg') # for writing to files only
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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def check_pil_font(font=FONT, size=10):
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# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
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font = Path(font)
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font = font if font.exists() else (CONFIG_DIR / font.name)
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try:
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return ImageFont.truetype(str(font) if font.exists() else font.name, size)
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except Exception: # download if missing
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try:
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check_font(font)
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return ImageFont.truetype(str(font), size)
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except TypeError:
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check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
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except URLError: # not online
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return ImageFont.load_default()
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class Annotator:
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# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
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def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
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assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
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non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
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self.pil = pil or non_ascii
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if self.pil: # use PIL
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
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size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
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else: # use cv2
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self.im = im
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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
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# Add one xyxy box to image with label
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if self.pil or not is_ascii(label):
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self.draw.rectangle(box, width=self.lw, outline=color) # box
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if label:
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w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0
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# _, _, w, h = self.font.getbbox(label) # text width, height (New)
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outside = box[1] - h >= 0 # label fits outside box
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self.draw.rectangle(
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(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
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box[1] + 1 if outside else box[1] + h + 1),
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fill=color,
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)
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# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
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self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
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else: # cv2
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p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
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if label:
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tf = max(self.lw - 1, 1) # font thickness
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w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
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outside = p1[1] - h >= 3
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p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
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cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(self.im,
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label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0,
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self.lw / 3,
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txt_color,
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thickness=tf,
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lineType=cv2.LINE_AA)
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def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
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"""Plot masks at once.
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Args:
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masks (tensor): predicted masks on cuda, shape: [n, h, w]
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colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
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im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
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alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
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"""
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if self.pil:
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# convert to numpy first
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self.im = np.asarray(self.im).copy()
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if len(masks) == 0:
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self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
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colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
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colors = colors[:, None, None] # shape(n,1,1,3)
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masks = masks.unsqueeze(3) # shape(n,h,w,1)
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masks_color = masks * (colors * alpha) # shape(n,h,w,3)
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inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
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mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
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im_gpu = im_gpu.flip(dims=[0]) # flip channel
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im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
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im_gpu = im_gpu * inv_alph_masks[-1] + mcs
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im_mask = (im_gpu * 255).byte().cpu().numpy()
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self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
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if self.pil:
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# convert im back to PIL and update draw
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self.fromarray(self.im)
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def rectangle(self, xy, fill=None, outline=None, width=1):
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# Add rectangle to image (PIL-only)
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self.draw.rectangle(xy, fill, outline, width)
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def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
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# Add text to image (PIL-only)
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if anchor == 'bottom': # start y from font bottom
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w, h = self.font.getsize(text) # text width, height
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xy[1] += 1 - h
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self.draw.text(xy, text, fill=txt_color, font=self.font)
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def fromarray(self, im):
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# Update self.im from a numpy array
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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def result(self):
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# Return annotated image as array
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return np.asarray(self.im)
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def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
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"""
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x: Features to be visualized
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module_type: Module type
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stage: Module stage within model
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n: Maximum number of feature maps to plot
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save_dir: Directory to save results
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"""
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if 'Detect' not in module_type:
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batch, channels, height, width = x.shape # batch, channels, height, width
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if height > 1 and width > 1:
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f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
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blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
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n = min(n, channels) # number of plots
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fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
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ax = ax.ravel()
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plt.subplots_adjust(wspace=0.05, hspace=0.05)
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for i in range(n):
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ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
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ax[i].axis('off')
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LOGGER.info(f'Saving {f}... ({n}/{channels})')
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plt.savefig(f, dpi=300, bbox_inches='tight')
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plt.close()
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np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
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def hist2d(x, y, n=100):
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# 2d histogram used in labels.png and evolve.png
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xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
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hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
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xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
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yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
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return np.log(hist[xidx, yidx])
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def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
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from scipy.signal import butter, filtfilt
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# https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
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def butter_lowpass(cutoff, fs, order):
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nyq = 0.5 * fs
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normal_cutoff = cutoff / nyq
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return butter(order, normal_cutoff, btype='low', analog=False)
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b, a = butter_lowpass(cutoff, fs, order=order)
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return filtfilt(b, a, data) # forward-backward filter
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def output_to_target(output, max_det=300):
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# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
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targets = []
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for i, o in enumerate(output):
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box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
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j = torch.full((conf.shape[0], 1), i)
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targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
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return torch.cat(targets, 0).numpy()
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@threaded
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def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
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# Plot image grid with labels
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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if isinstance(targets, torch.Tensor):
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targets = targets.cpu().numpy()
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max_size = 1920 # max image size
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max_subplots = 16 # max image subplots, i.e. 4x4
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bs, _, h, w = images.shape # batch size, _, height, width
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bs = min(bs, max_subplots) # limit plot images
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ns = np.ceil(bs ** 0.5) # number of subplots (square)
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if np.max(images[0]) <= 1:
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images *= 255 # de-normalise (optional)
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# Build Image
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mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
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for i, im in enumerate(images):
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if i == max_subplots: # if last batch has fewer images than we expect
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break
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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im = im.transpose(1, 2, 0)
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mosaic[y:y + h, x:x + w, :] = im
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# Resize (optional)
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scale = max_size / ns / max(h, w)
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if scale < 1:
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h = math.ceil(scale * h)
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w = math.ceil(scale * w)
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mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
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# Annotate
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fs = int((h + w) * ns * 0.01) # font size
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annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
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for i in range(i + 1):
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
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if paths:
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annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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if len(targets) > 0:
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ti = targets[targets[:, 0] == i] # image targets
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boxes = xywh2xyxy(ti[:, 2:6]).T
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classes = ti[:, 1].astype('int')
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labels = ti.shape[1] == 6 # labels if no conf column
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conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
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if boxes.shape[1]:
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if boxes.max() <= 1.01: # if normalized with tolerance 0.01
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boxes[[0, 2]] *= w # scale to pixels
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boxes[[1, 3]] *= h
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elif scale < 1: # absolute coords need scale if image scales
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boxes *= scale
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boxes[[0, 2]] += x
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boxes[[1, 3]] += y
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for j, box in enumerate(boxes.T.tolist()):
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cls = classes[j]
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color = colors(cls)
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cls = names[cls] if names else cls
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
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annotator.box_label(box, label, color=color)
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annotator.im.save(fname) # save
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def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
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# Plot LR simulating training for full epochs
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optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
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y = []
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for _ in range(epochs):
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scheduler.step()
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y.append(optimizer.param_groups[0]['lr'])
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plt.plot(y, '.-', label='LR')
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plt.xlabel('epoch')
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plt.ylabel('LR')
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plt.grid()
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plt.xlim(0, epochs)
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plt.ylim(0)
|
||
|
|
plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
|
||
|
|
def plot_val_txt(): # from utils.plots import *; plot_val()
|
||
|
|
# Plot val.txt histograms
|
||
|
|
x = np.loadtxt('val.txt', dtype=np.float32)
|
||
|
|
box = xyxy2xywh(x[:, :4])
|
||
|
|
cx, cy = box[:, 0], box[:, 1]
|
||
|
|
|
||
|
|
fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
|
||
|
|
ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
|
||
|
|
ax.set_aspect('equal')
|
||
|
|
plt.savefig('hist2d.png', dpi=300)
|
||
|
|
|
||
|
|
fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
|
||
|
|
ax[0].hist(cx, bins=600)
|
||
|
|
ax[1].hist(cy, bins=600)
|
||
|
|
plt.savefig('hist1d.png', dpi=200)
|
||
|
|
|
||
|
|
|
||
|
|
def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
|
||
|
|
# Plot targets.txt histograms
|
||
|
|
x = np.loadtxt('targets.txt', dtype=np.float32).T
|
||
|
|
s = ['x targets', 'y targets', 'width targets', 'height targets']
|
||
|
|
fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
|
||
|
|
ax = ax.ravel()
|
||
|
|
for i in range(4):
|
||
|
|
ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
|
||
|
|
ax[i].legend()
|
||
|
|
ax[i].set_title(s[i])
|
||
|
|
plt.savefig('targets.jpg', dpi=200)
|
||
|
|
|
||
|
|
|
||
|
|
def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()
|
||
|
|
# Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
|
||
|
|
save_dir = Path(file).parent if file else Path(dir)
|
||
|
|
plot2 = False # plot additional results
|
||
|
|
if plot2:
|
||
|
|
ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
|
||
|
|
|
||
|
|
fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
|
||
|
|
# for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
|
||
|
|
for f in sorted(save_dir.glob('study*.txt')):
|
||
|
|
y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
|
||
|
|
x = np.arange(y.shape[1]) if x is None else np.array(x)
|
||
|
|
if plot2:
|
||
|
|
s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
|
||
|
|
for i in range(7):
|
||
|
|
ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
|
||
|
|
ax[i].set_title(s[i])
|
||
|
|
|
||
|
|
j = y[3].argmax() + 1
|
||
|
|
ax2.plot(y[5, 1:j],
|
||
|
|
y[3, 1:j] * 1E2,
|
||
|
|
'.-',
|
||
|
|
linewidth=2,
|
||
|
|
markersize=8,
|
||
|
|
label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
|
||
|
|
|
||
|
|
ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
|
||
|
|
'k.-',
|
||
|
|
linewidth=2,
|
||
|
|
markersize=8,
|
||
|
|
alpha=.25,
|
||
|
|
label='EfficientDet')
|
||
|
|
|
||
|
|
ax2.grid(alpha=0.2)
|
||
|
|
ax2.set_yticks(np.arange(20, 60, 5))
|
||
|
|
ax2.set_xlim(0, 57)
|
||
|
|
ax2.set_ylim(25, 55)
|
||
|
|
ax2.set_xlabel('GPU Speed (ms/img)')
|
||
|
|
ax2.set_ylabel('COCO AP val')
|
||
|
|
ax2.legend(loc='lower right')
|
||
|
|
f = save_dir / 'study.png'
|
||
|
|
print(f'Saving {f}...')
|
||
|
|
plt.savefig(f, dpi=300)
|
||
|
|
|
||
|
|
|
||
|
|
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
|
||
|
|
def plot_labels(labels, names=(), save_dir=Path('')):
|
||
|
|
# plot dataset labels
|
||
|
|
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
|
||
|
|
c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
|
||
|
|
nc = int(c.max() + 1) # number of classes
|
||
|
|
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
|
||
|
|
|
||
|
|
# seaborn correlogram
|
||
|
|
sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
|
||
|
|
plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
# matplotlib labels
|
||
|
|
matplotlib.use('svg') # faster
|
||
|
|
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
|
||
|
|
y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
|
||
|
|
with contextlib.suppress(Exception): # color histogram bars by class
|
||
|
|
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
|
||
|
|
ax[0].set_ylabel('instances')
|
||
|
|
if 0 < len(names) < 30:
|
||
|
|
ax[0].set_xticks(range(len(names)))
|
||
|
|
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
|
||
|
|
else:
|
||
|
|
ax[0].set_xlabel('classes')
|
||
|
|
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
|
||
|
|
sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
|
||
|
|
|
||
|
|
# rectangles
|
||
|
|
labels[:, 1:3] = 0.5 # center
|
||
|
|
labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
|
||
|
|
img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
|
||
|
|
for cls, *box in labels[:1000]:
|
||
|
|
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
|
||
|
|
ax[1].imshow(img)
|
||
|
|
ax[1].axis('off')
|
||
|
|
|
||
|
|
for a in [0, 1, 2, 3]:
|
||
|
|
for s in ['top', 'right', 'left', 'bottom']:
|
||
|
|
ax[a].spines[s].set_visible(False)
|
||
|
|
|
||
|
|
plt.savefig(save_dir / 'labels.jpg', dpi=200)
|
||
|
|
matplotlib.use('Agg')
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
|
||
|
|
def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
|
||
|
|
# Show classification image grid with labels (optional) and predictions (optional)
|
||
|
|
from utils.augmentations import denormalize
|
||
|
|
|
||
|
|
names = names or [f'class{i}' for i in range(1000)]
|
||
|
|
blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
|
||
|
|
dim=0) # select batch index 0, block by channels
|
||
|
|
n = min(len(blocks), nmax) # number of plots
|
||
|
|
m = min(8, round(n ** 0.5)) # 8 x 8 default
|
||
|
|
fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
|
||
|
|
ax = ax.ravel() if m > 1 else [ax]
|
||
|
|
# plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
||
|
|
for i in range(n):
|
||
|
|
ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
|
||
|
|
ax[i].axis('off')
|
||
|
|
if labels is not None:
|
||
|
|
s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
|
||
|
|
ax[i].set_title(s, fontsize=8, verticalalignment='top')
|
||
|
|
plt.savefig(f, dpi=300, bbox_inches='tight')
|
||
|
|
plt.close()
|
||
|
|
if verbose:
|
||
|
|
LOGGER.info(f"Saving {f}")
|
||
|
|
if labels is not None:
|
||
|
|
LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
|
||
|
|
if pred is not None:
|
||
|
|
LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
|
||
|
|
return f
|
||
|
|
|
||
|
|
|
||
|
|
def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
|
||
|
|
# Plot evolve.csv hyp evolution results
|
||
|
|
evolve_csv = Path(evolve_csv)
|
||
|
|
data = pd.read_csv(evolve_csv)
|
||
|
|
keys = [x.strip() for x in data.columns]
|
||
|
|
x = data.values
|
||
|
|
f = fitness(x)
|
||
|
|
j = np.argmax(f) # max fitness index
|
||
|
|
plt.figure(figsize=(10, 12), tight_layout=True)
|
||
|
|
matplotlib.rc('font', **{'size': 8})
|
||
|
|
print(f'Best results from row {j} of {evolve_csv}:')
|
||
|
|
for i, k in enumerate(keys[7:]):
|
||
|
|
v = x[:, 7 + i]
|
||
|
|
mu = v[j] # best single result
|
||
|
|
plt.subplot(6, 5, i + 1)
|
||
|
|
plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
|
||
|
|
plt.plot(mu, f.max(), 'k+', markersize=15)
|
||
|
|
plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
|
||
|
|
if i % 5 != 0:
|
||
|
|
plt.yticks([])
|
||
|
|
print(f'{k:>15}: {mu:.3g}')
|
||
|
|
f = evolve_csv.with_suffix('.png') # filename
|
||
|
|
plt.savefig(f, dpi=200)
|
||
|
|
plt.close()
|
||
|
|
print(f'Saved {f}')
|
||
|
|
|
||
|
|
|
||
|
|
def plot_results(file='path/to/results.csv', dir=''):
|
||
|
|
# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
|
||
|
|
save_dir = Path(file).parent if file else Path(dir)
|
||
|
|
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
|
||
|
|
ax = ax.ravel()
|
||
|
|
files = list(save_dir.glob('results*.csv'))
|
||
|
|
assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
|
||
|
|
for f in files:
|
||
|
|
try:
|
||
|
|
data = pd.read_csv(f)
|
||
|
|
s = [x.strip() for x in data.columns]
|
||
|
|
x = data.values[:, 0]
|
||
|
|
for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
|
||
|
|
y = data.values[:, j].astype('float')
|
||
|
|
# y[y == 0] = np.nan # don't show zero values
|
||
|
|
ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
|
||
|
|
ax[i].set_title(s[j], fontsize=12)
|
||
|
|
# if j in [8, 9, 10]: # share train and val loss y axes
|
||
|
|
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
||
|
|
except Exception as e:
|
||
|
|
LOGGER.info(f'Warning: Plotting error for {f}: {e}')
|
||
|
|
ax[1].legend()
|
||
|
|
fig.savefig(save_dir / 'results.png', dpi=200)
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
|
||
|
|
def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
|
||
|
|
# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
|
||
|
|
ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
|
||
|
|
s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
|
||
|
|
files = list(Path(save_dir).glob('frames*.txt'))
|
||
|
|
for fi, f in enumerate(files):
|
||
|
|
try:
|
||
|
|
results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
|
||
|
|
n = results.shape[1] # number of rows
|
||
|
|
x = np.arange(start, min(stop, n) if stop else n)
|
||
|
|
results = results[:, x]
|
||
|
|
t = (results[0] - results[0].min()) # set t0=0s
|
||
|
|
results[0] = x
|
||
|
|
for i, a in enumerate(ax):
|
||
|
|
if i < len(results):
|
||
|
|
label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
|
||
|
|
a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
|
||
|
|
a.set_title(s[i])
|
||
|
|
a.set_xlabel('time (s)')
|
||
|
|
# if fi == len(files) - 1:
|
||
|
|
# a.set_ylim(bottom=0)
|
||
|
|
for side in ['top', 'right']:
|
||
|
|
a.spines[side].set_visible(False)
|
||
|
|
else:
|
||
|
|
a.remove()
|
||
|
|
except Exception as e:
|
||
|
|
print(f'Warning: Plotting error for {f}; {e}')
|
||
|
|
ax[1].legend()
|
||
|
|
plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
|
||
|
|
|
||
|
|
|
||
|
|
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||
|
|
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
|
||
|
|
xyxy = torch.tensor(xyxy).view(-1, 4)
|
||
|
|
b = xyxy2xywh(xyxy) # boxes
|
||
|
|
if square:
|
||
|
|
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
|
||
|
|
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
|
||
|
|
xyxy = xywh2xyxy(b).long()
|
||
|
|
clip_boxes(xyxy, im.shape)
|
||
|
|
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
|
||
|
|
if save:
|
||
|
|
file.parent.mkdir(parents=True, exist_ok=True) # make directory
|
||
|
|
f = str(increment_path(file).with_suffix('.jpg'))
|
||
|
|
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
|
||
|
|
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
|
||
|
|
return crop
|